Sort by
Refine Your Search
-
biodiversity and sustainability research. The initial objective is to use deep learning techniques to perform acoustic species identification in real-time on low-cost sensing devices coupled to cloud-based
-
Although deep learning has produces state of the art results on many problems, it is a data hungry technology requiring a lot of human supervision in the form of annotated data. Potential PhD topic
-
Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic
-
process by leveraging deep learning including automated app functionality summarization [1], UI design generation [2], and front-end code generation [3]. We hope to explore more in this direction including
-
propose a model to proactively locate accessibility issues and recommend potential fixes to their app based on deep learning models. On the other hand, we will also propose a new approach based on
-
. This is indeed what AIC, BIC, MDL and MML would anticipate. And yet deep learning methods can often work despite this. This project investigates how deep learning can survive over-fitting and whether
-
This Ph.D. project aims to combine causal analysis with deep learning for mental health support. As deep learning is vulnerable to spurious correlations, novel causal discovery and inference methods
-
everyone is supported to succeed. Learn more about Monash . The Opportunity This is an opportunity to join the Deputy Vice-Chancellor (International) Portfolio at Monash University where you will be central
-
everyone's contributions, lived experience, and expertise. That’s why we champion an inclusive and respectful workplace culture where everyone is supported to succeed. Learn more about Monash . The
-
the tuition fee scholarship and Single Overseas Health Cover (OSHC) for the successful international awardee. The Opportunity A full PhD Scholarship is currently available for research into deep learning